Abstract For millions of people with swallowing disorders, preventing potentially deadly aspiration pneumonia requires following pre-scribed safe eating strategies. But adherence is poor, and caregivers’ ability to encourage adherence is limited by the onerouand socially aversive need to monitoring another’s eating. We have developed an early prototype for an intelligent assistant monitors adherence and provides feedback to the patient, and tested monitoring precision with healthy subjects for one stratcalled a “chin tuck.” Results indicate that adaptations of currgeneration machine vision and personal assistant technologies ceffectively monitor chin tuck adherence, and suggest feasibilita more general assistant that encourages adherence to a range o safe eating strategies. Dysphagia Patients Need Help With Adherence Dysphagia, or difficulty swallowing, is a widespread and often devastating disorder that affects 10–30% of the elderly population and high percentages of patients with neurological conditions such as stroke (50–75%) and Parkinson’s Disease (up to 95%). Dysphagia creates numerous risks; chief among them is aspiration pneumonia—an infection caused by accidental ingestion of bacteria-laden food into the lungs with mortality ranging from 10–70% (DeLegge, 2002). Clinicians frequently prescribe risk-reducing compensatory strategies such as tucking the chin to the chest before swallowing to protect the airway, and making an “effortful swallow” to clear residual food in the pharynx. These strategies have been shown to significantly improve patienthealth and well-being (Low et al., 2001). However, few Figure 1. Dysphagia Coach prototype showin